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A CNN-Based Smart Waste Management System Using
TensorFlow Lite and LoRa-GPS Shield in Internet of Things
Environment
NICHOLAS CHIENG ANAK SALLANG1, MOHAMMAD TARIQUL ISLAM1, (Senior Member, IEEE),
MOHAMMAD SHAHIDUL ISLAM1, (Graduate Student Member, IEEE), & Haslina Arshad2
1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Malaysia
2Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Corresponding author: MOHAMMAD TARIQUL ISLAM (tariqul@ukm.edu.my)
This work was supported by the Universiti Kebangsaan Malaysia, Malaysia.
ABSTRACT Urban areas are facing challenges in waste management systems due to the rapid growth of
population in cities, causing huge amount of waste generation. As traditional waste management system is highly
inefficient and costly, the waste of resources can be utilized efficiently with the integration of the internet of things
(IoT) and deep learning model. The main purpose of this research is to develop a smart waste management system
using the deep learning model that improves the waste segregation process and enables monitoring of bin status in
an IoT environment. The SSD MobileNetV2 Quantized is used and trained with the dataset that consists of paper,
cardboard, glass, metal, and plastic for waste classification and categorization. By integrating the trained model
on TensorFlow Lite and Raspberry Pi 4, the camera module detects the waste and the servo motor, connected to a
plastic board, categorizes the waste into the respective waste compartment. The ultrasonic sensor monitors the
waste fill percentage, and a GPS module obtains the real-time latitude and longitude. The LoRa module on the
smart bin sends the status of the bin to the LoRa receiver at 915 MHz. The electronic components of the smart bin
are protected with RFID based locker, where only the registered RFID tag can be used to unlock for maintenance
or upgrading purposes.
INDEX TERMS Waste Classification, CNN, Object Detection, LoRa-GPS Shield, Internet of Things
I. INTRODUCTION
As the population living in urban areas increases rapidly
throughout the years, there are a lot of challenges taken place in
cities, especially in the waste management system. The World
Bank stated that approximately 2.01 billion tons of waste were
generated in 2016 due to the population and economic growth in
the urban areas, which is estimated to increase to 3.40 billion tons
in 2050 [1]. As per EUROSTAT, the European Union recycled 423
million tons of waste, which are 56% of locally produced waste in
2016 [2]. Moreover, 24% of the locally produced waste of 179
million tons was landfilled in the European Union. The European
Union is among the world’s largest landfills, which are Laogang in
Shanghai, Bordo Poniente in Mexico City, Jardin Gramacho in Rio
de Janeiro, and Sudokwon in Seoul demonstrated that this is often
a worldwide issue [3-5]. Waste management, also considered as a
waste collection system, requires several steps and actions to
manage waste disposal, including the collection, transport,
monitoring and regulation of the whole process. The methods to
manage waste among urban and rural areas are different.
Generally, the best solution to manage collected waste is to reuse
and recycle them. However, the cost of effective waste
management is high, which requires cooperation from authorities
and users.
A lot of efforts have been made by the government or
authorities to improve the waste management system; nevertheless,
this is still a big problem in every country, especially in urban
cities. Two kinds of situations happen if the waste is collected
based on schedule; either the bins are not fully filled, or it is
overflowed. The waste that is collected before the bins are full
would lead to waste of manpower resources. Otherwise, the
overflow of the waste would cause environmental pollution,
including air pollution and infectious diseases. Another effort to
reduce waste production and mitigate the environment is recycling
the waste. However, this method does not show positive results due
to the ignorance of the users who do not categorize the waste
correctly. The rapid development of the digital world brings a
massive impact on technical developments, especially by allowing
intelligence to be integrated into the existing technologies [6-8],
which is also called the Internet of Things (IoT). The technologies
combined with IoT lead the further development of various fields,
such as engineering, to an entirely new perspective [9]. There are
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roughly 127 devices connected to the internet every second, which
is equivalent to 328 million devices connected per month [5, 10].
The expected IoT market in 2023 will achieve 1.1 trillion [11].
Such statistics have shown the imperative role of IoT in the modern
world. IoT enables the control of things in the real world and
informs things by unifying everything in the real world under a
common infrastructure [8, 12]. With the help of IoT, the current
waste management system can be improved in many aspects such
as cost of resources, user-friendly, easy to be managed, reduce the
waste to be disposed of by recycling them. Hence, the waste
management system integrated with IoT has developed in the last
decade.
Apart from IoT, another technology that has developed and
brought a huge impact is “machine learning”. Machine learning
enables machines or computers to learn unsupervised from data
that is labelled or unlabelled. Machine learning involves certain
models and algorithms that are predictive of scientific study. The
trend of machine learning has achieved the highest peak due to
offering the most impressive computing features. By referring to
Tractica’s recent statistics [10], the market size of machine learning
and AI-based technology was 1.4 billion dollars in 2016, and by
2025, growth will increase by 59.8 billion dollars. These statistics
prove the effectiveness of machine learning-based applications.
Deep neural network results showed improved accuracy in a series
of relevant benchmark competitions in machine learning and
pattern recognition [13]. Deep learning is a form of machine
learning that allows the learning of computers from experience
[14]. Deep learning enables multiple layers of processing in
computational models to understand a data presentation with
multiple layers of abstraction. These technologies help us to build
cutting-edge technology in fields such as voice recognition, visual
objects, drug discovery and genomics in numerous different fields.
There is a class of deep learning called convolutional neural
network (CNN) which is applied mainly for image processing,
object recognition and so on. The integration of CNN in a smart
waste management system can highly improve the performance of
waste classification and categorize them correctly, saving
resources and reducing the waste generated in the world. This paper
presents a smart waste management system that detects and
categorize the different types of waste and places them into a
specific compartment. The SSD MobileNetV2 has been applied to
the system for detection purposes. Moreover, the system is
integrated into the IoT based sensors and LoRa-GPS module that
effectively identify the bin location and transfer the bin status over
a long distance.
II. RELATED WORK
The status of the bin, especially the fill percentage of the waste
inside, should have less power consumption. Different methods
used to monitor the level of waste in the dustbin are proposed by
several authors, including an infrared sensor to measure the
distance by reflecting light waves and ultrasonic sensor measures
with the principle of reflected sound waves. Navghane et al.
proposed a method to reduce the cost and increase the efficiency
of waste applications [15]. A dustbin is interfaced with a
microcontroller-based system with IR wireless systems and a
central system displaying current garbage status. Therefore, the
HTML page that updates the status can reduce human resources
and efforts. Another GSM electronic monitoring system is
proposed by Aasim et al., which sends SMS to the authority that
the dustbin is fully filled to send the truck for trash collection
[16]. Ultrasonic sensors were used to detect the amount of trash
in the dustbin, and the GSM module was to provide information
on the dustbin status. However, this system is only able to detect
the top of the garbage level and cannot realize the space left in
the dustbin.
To monitor the status of the bin, an energy-efficient
telecommunication protocol that can travel far distances is
important to be integrated in the smart waste management
system. There are various kinds of telecommunication protocols
available and each of them has its own strength and weakness in
different situations. Smart garbage bins [17] enable approved
persons to obtain information regarding the filling level via
ultrasonic sensors and a GSM-equipped microcontroller that
sends data to a control station. Another similar study [18]
involves several sensors such as ultrasonic sensor, moisture
sensor and gas sensor in its system to monitor the waste and
condition of the bin. The ultrasonic sensor is used to monitor the
garbage level. The wet waste can be detected by moisture sensors,
and toxic gases can be detected through gas sensors. The
microcontroller obtains the data from sensors and transmits them
through the ZigBee transmitter at a long distance. Besides, the
microcontroller also sends the SMS message to the mobile device
through GSM. Apart from that, a sensor node for monitoring the
waste bin filling level equipped with RFID technology is
proposed in 2016, which could be a feasible solution due to its
robustness and low cost [19]. However, 2G is not a long-term
solution because it has a high running cost and will be eliminated
in the future [20], causing the 2G telecommunication to stop
servicing. This will lead to the disability to use IoT that
communicates through GSM protocols. Table I shows the
transmission range, spectrum used, bandwidth and maximum
data rate of various Low Power Wide Area Network (LPWAN)
technologies. LoRa provides a free spectrum under 1 GHz;
meanwhile, it can transmit up to 15km. A system to monitor the
overall condition of the dustbins plays an important role in a
smart waste management system, where the authorities can
monitor the overall situation of all dustbins in an easier method.
Misra et al. proposed a waste management system that is
monitored by the cloud [21]. From their experiment, ultrasonic
sensors are used to sense the level of waste in the dustbin due to
the longer range provided compared to IR sensors. Apart from
that, IR sensors are also found to be affected by sunlight, object
colour and object hardness. Their system is capable of sensing the
amount of waste and the strength of biogas generated in the
municipal area. The information gathered by sensors is sent to a
server, where it is stored and processed over the internet. This
data is then used to track the waste bins, and the correct choice is
made by selecting the correct waste bin to be collected. The main
features of this system are that it is designed to learn from
experience and to draw conclusions not only on the status of the
daily waste level. Apart from that, based on the experience, the
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system will predict the future situation, such as the availability of
vehicles near the site and other factors involved. The overall cost
and power consumption of this system is controlled very well, but
it cannot recognize and separate the various types of waste in the
dustbin.
Table I. Comparison of various LPWAN technologies [5, 22-24]
LoRa
GSM
(Rel. 8)
EC-
GSM-
IoT
(Rel. 13)
LTE
(Rel. 8)
Emtc
(Rel.
13)
NB-IoT
(Rel.
13)
Range
Max.
coupli
ng loss
<15km
155dB
<35km
144dB
<35km
164dB
<100k
m
144dB
<100km
156dB
<35km
164dB
Spectr
um
Unlice
nsed <
1GHz
Licens
ed
GSM
bands
Licensed
GSM
bands
License
d LTE
bands
In-band
License
d LTE
bands
In-band
License
d LTE
in-band
guard-
band
stand-
alone
Bandw
idth
<500k
Hz
200kH
z
200kHz
LTE
carrier
bandwi
dth (1.4
–
20MHz
)
1.08
MHz
(1.4MH
z carrier
bandwi
dth)
180kHz
(200kH
z carrier
bandwi
dth)
Max.
data
rate
<50kbp
s
(DL/U
L)
<500kb
ps
(DL/U
L)
<140kbp
s
(DL/UL)
<10Mb
ps (DL)
<5Mbp
s (UL)
<1Mbps
(DL/UL
)
<170kb
ps (DL)
<250kb
ps (UL)
Apart from that, Bhor et al. proposed a method for Smart
Garbage Management in Smart Cities using IoT which can
monitor the system through GUI [25]. The bins are integrated
with ultrasonic sensors to detect the amount of garbage inside and
a GSM system to communicate to the authorized control room.
To have better control over the disposal of garbage, a GUI is built
to track the desired details relevant to the garbage for various
selected locations. This system ensures that waste in the dustbins
are collected shortly after the amount of garbage reaches its limit.
Apart from that, this system also eliminates corruption in the
overall waste management system by detecting false reports. The
vehicle garbage collection trips have been reduced and thereby
reduces the overall waste collection budget. A smart waste
management system requires automation to alert the authorities
on the condition of the dustbins when the level of waste is almost
or already full. A smart garbage alert system was proposed by
Norfadzlia et al., which is an integrated system consisting of
Arduino Uno, GSM Module, Ultrasonic sensor and LED light
[26]. The ultrasonic sensors are used to detect two threshold
levels which are 70% and 90% of the bin height. When the first
threshold level is reached, the green LEDs will be switched on to
alert the residents on that floor, and a first warning message is
sent to the municipality. If the garbage level is then reached the
second threshold level, the second warning message is sent to the
municipality and red LEDs will be turned on to alert the residents.
However, this system is limited and user friendly to users in flat
residential areas or condominiums.
Another smart garbage alert system presented by Kumar et
al. involves a microcontroller and IoT to alert the administrator.
The system will alert a web server using a microcontroller and
telecommunication module [27]. The microcontroller, Arduino
UNO R3 is used to read data from an ultrasonic sensor. After the
garbage crosses a certain level, it is configured to send a warning
to the Thing Speak web server. For the verification process, an
RFID reader is interconnected to the Arduino. Whenever the
RFID reader is interrupted by an RFID tag (ID card of the
cleaner), the ultrasonic sensor checks the dustbin’s status and
sends it to the webserver. An android application is created to
view the notification and status at the server end. The limitation
of this system is the status of the bin can only be seen when the
RFID tag is detected manually by the RFID reader, which is not
user-friendly [28]. Due to the number of dustbins allocated in the
urban areas is in a huge number, the power consumed by the
dustbins and the system need to be handle properly to prevent
overconsumption of resources. An article with title ‘A Low
Power IoT Sensor Node Architecture for Waste Management
Within Smart Cities Context’ focuses on the development of an
Internet of Things (IoT) system to improve power-saving waste
management in the context of Smart Cities [29]. An innovative
typology of sensor nodes based on the use of low-cost and low-
power components is defined. This node is integrated with a
single-chip microcontroller, a sensor capable of measuring the
filling level of the trash bins using ultrasound and a LoRa
LPWAN (Low Power Large Area Network) technology-based
data transmission module [22]. A minimal network architecture,
based on a LoRa gateway, was built along with the node to test
the performance of the IoT node. In particular, the paper analyses
the node architecture in-depth, focusing on energy-saving
technologies and policies, with the goal of extending battery life
by reducing power consumption through optimization of
hardware and software. Apart from that, the author also analyses
the effectiveness of the sensor and radio module in the system.
However, the proposed system does not categorize the waste
automatically that leads to the biodegradable and non-
biodegradable waste being mixed up in the bin.
The smart waste management system integrated with IoT
only is not sufficient to achieve good management of waste. This
is due to the waste not being categorized and separated in order
to decide whether it can be recycled. An intelligent waste
management system with a smart bin is necessary to manage a
variety of waste materials. In artificial intelligence systems,
object detection has been widely used. Recently, more studies
have focused on improving the object detection techniques using
deep machine learning, such as vehicle detection [30], face
detection [31], and document image classification [32]. The most
widely used technique is Convolutional Neural Network (CNN).
Bobulski et al. created a waste classification system using image
processing and CNN to classify various kinds of plastic garbage
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[33]. This plastic waste segregation system improves the
efficiency of recycling by automating the sorting of materials,
thus reducing the cost and simplifying the process. They
developed a simpler and hence faster 15-layer network compared
to AlexNet. This network has a shorter learning time, and in their
research, this system categorized the waste into four main
categories with very high accuracy. However, the proposed 15-
layer network and AlexNet have less depth than other existing
models, leading to difficulties learning features from image sets.
Nowakowski et al. proposed an idea to identify and classify waste
electrical and electronic equipment from photos by using an
image recognition system [34]. The system involves users to
improve the classification of the system by capturing their e-
waste objects and upload them to the waste collection company
server. The system will study the waste and enhance the waste
collection preparation. This image recognition system can run on
a server or via a mobile app. The authors proposed various
methods for a different types of images. A convolutional neural
network (CNN) based model is used to identify the type of e-
waste; meanwhile, the category and size of waste are detected by
a faster region-based convolutional neural network (Faster R-
CNN). CNN is good in image classification, while R-CNN is
mainly for object detection. Yet, R-CNN must feed 2000 regions
and apply CNN for each region, which consumes a lot of time to
train for a large dataset and affect the speed of detection.
A deep neural network model, WasteNet, is proposed to
improve waste classification accuracy [35]. This model is
implemented on a Jetson Nano edge device which allows
convenient deployment at the edge to permit smart bins to
identify waste. On the TrashNet dataset, the WasteNet model has
enhanced the accuracy of the system to 97%. This is a significant
improvement on the original SVM method, which achieved 63%
accuracy and an accuracy of 22% for CNN. Transfer learning is
used on the WasteNet model to improve the baseline
performance, speed up overall model development and training
time. On the ImageNet dataset, the models which are trained for
general image classification are used for transfer learning in this
WasteNet model. A paper with title “Classification of Trash for
Recyclability Status” is proposed [36]. A dataset named TrashNet
is created and consists of 6 classes which are glass, paper, metal,
plastic, cardboard, and trash. Each of the class has around 400-
500 images and this dataset is released by them to the public. In
this research, support vector machines (SVM) and convolutional
neural networks (CNN) are used to test for this dataset on their
performance. Based on the result of the research, CNN performs
better than SVM. SVM has lower accuracy and limitation on the
type of waste detectable. This is due to the simpler algorithm in
SVM compared to the neural network, where a longer time is
required to train the model to achieve optimal performance. Apart
from that, Adedeji et al. proposed an intelligent waste
classification system by combination of ResNet and SVM [37].
They used a 50-layer residual net pre-train (ResNet-50) CNN
model to serve as the extractor of the system and Support Vector
Machine (SVM) to categorize the waste into various groups such
as glass, metal, paper, and plastic etc. A dataset of images of trash
which was developed by Gary et al. is used to test the accuracy
of this system, and an accuracy of 87% is achieved in this
research. Wei-Lung et al. proposed an interesting idea in order to
improve the accuracy of the classification of recycling waste [38].
TrashNet, a dataset consisting of six types of waste categories and
contains up to 2527 waste pictures, was used in this research to
test the CNNs’ performance. Data augmentation is applied to the
dataset in order to significantly increase the diversity of data
available for the training model and yet without collecting new
data. Apart from that, a genetic algorithm (GA) is utilized in this
research on the fully connected layer of DenseNet121. This can
improve the accuracy of DenseNet121 on classification, and this
optimized DenseNet121 achieved 99.6%, the highest accuracy in
their research. From the papers reviewed, the proposed waste
management systems are insufficient to solve the major
challenges faced in cities. Most of the system proposed only has
a single function, such as the system is only able to monitor the
level of waste without method to alert the administrator. Apart
from that, some systems only able to transmit the data of the bin
in a short distance such as Wi-Fi protocol. The lack of
classification and categorization of waste in the proposed system
by many authors is also unable to solve the recycling problem
existing.
III. SMART WASTE MANAEMENT SYSTEM
A. System Model Design
The design and dimensions of the bin are shown in Figure
1. The top compartment, also known as the electronic component
compartment, stores most of the electronic components. The
remaining compartments are used to store different types of
waste. The waste thrown onto temporary waste placement will be
detected by Raspberry Pi and then moved into the respective
compartment by using servo motors.
Figure 1. Design and dimensions of the proposed bin
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Figure 2. Overview of the smart waste management system
Figure 2 shows the overview of the smart waste
management system. The development of a smart waste
management system focuses on waste classification,
categorization and bin status monitoring. There will be five major
steps in developing the CNN-based object detection: choosing
TensorFlow Lite over TensorFlow, the model and architecture of
object detection, method of obtaining dataset, and method to
export the trained model into hardware application. The waste
classification and categorization system include CNN based
object detection model and hardware such as Raspberry Pi,
camera module, ultrasonic sensor and servo motors. Apart from
that, the monitoring system of the bin is built on Arduino with
ultrasonic sensors, GPS module and LoRa communication
module with a written Arduino IDE sketch algorithm to obtain
the real-time information of the bin from a further position.
Besides, RFID based locker system is also integrated with the
Arduino to protect the electronic components of the bin.
B. Object Detection Model
TensorFlow Lite is chosen over TensorFlow to be used on a
low power mobile platform. This is due to most of the models
trained on TensorFlow required a decent GPU to perform object
detection. However, the requirement of a decent GPU is not
applicable to the development of a smart bin. TensorFlow Lite
allows the object detection models to be used on low power
mobile devices such as Raspberry Pi. There are several pre-
trained detection models on the COCO dataset provided by
Tensorflow [39]. Several requirements need to be considered in
choosing the suitable and optimum object detection model. The
object detection model chosen is SSD MobileNetV2 Quantized
300×300, which is a COCO-trained model available in
TensorFlow. Single Shot MultiBox Detector, also known as SSD,
is specially designed for real-time object detection, which
performs much faster and is lighter in terms of CPU usage.
Figure 3 shows the architecture of the SSD MobileNet model,
where the layers are simplified to improve the performance
meanwhile maintain accuracy.
Figure 3. Architecture of SSD MobileNet model
Table II. Comparison among the different detection models
Method
mAP
FPS
Batch
size
# Boxes
Input
resolution
Faster R-
CNN
(VGG-16)
73.2
7
1
~ 6000
~ 1000 ×
600
Fast
YOLO
52.7
155
1
98
448×448
YOLO
66.4
21
1
98
448×448
SSD300
74.3
46
1
8732
300×300
SSD512
76.8
19
1
24564
512×512
SSD300
74.3
59
8
8732
300×300
SSD512
76.8
22
8
24564
512×512
Table III. Comparison among the different architectural models
Network
Top 1
Params
Multiply-
Adds
CPU
MobileNetV1
70.6
4.2M
575M
113ms
ShuffleNet
(1.5)
71.5
3.4M
292M
-
ShuffleNet
(x2)
73.7
5.4M
524M
-
NasNet-A
74.0
5.3M
564M
183ms
MobileNetV2
72.0
3.4M
300M
75ms
MobileNetV2
(1.4)
74.7
6.9M
585M
143ms
The SSD removes the region proposal network to increase
the frame rate of object detection and implements several
improvements such as multi-scale features and default boxes in
order to improve the accuracy of the model. By using images with
low resolution, such as 300 x 300 pixels, the time required to
detect an object is hugely reduced. From the comparison in Table
II, SSD has the optimum mean average precision (mAP) and high
frame rate among different detection models. Apart from the
detection model, the CNN architecture of MobileNetV2 is
designed to have decent classification performance on low power
mobile devices. MobileNet architecture substantially lowers the
network's complex structure and model size. MobileNetV2 has a
small architectural model size and low computing power
compared to other networks shown in Table III. The chosen
model in the proposed system is quantized. Common neural
networks consist of numerical values with high precision, which
leads to tens or hundred of million of weights. The extremely
large weights require a decent CPU, GPU or TPU to compute,
which consume huge computing power and large memory.
Quantization decreases the number of bits of image pixels
without affecting the accuracy by replacing the high-precision
numerical values with low-precision numerical values such as int
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and float. In this model, the 32-bit parameters is quantized to 8-
bit, where the size and performance of the model are improved
when performing detection. Two methods will be used to obtain
the dataset, which is download from free sources and capture by
phone with 12 megapixels camera module. The images will be
obtained from free resources on Google Images. Due to the SSD
MobileNetV2 Quantized 300×300, all the images in the waste
dataset shall be 300×300 pixels. However, the resolution of
images obtained are all in different sizes and format, thus an
open-source software, Batch Image Resize is used to resize all the
images to 300×300 pixels and output in JPEG image format. The
training of the waste detection model is based on supervised
learning, where the class of waste needs to be known by the
network. In machine learning, the process is called labelling,
which gives informative labels on the image to understand and
learn from it. An open-source software, LabelImg is used to label
the images into five categories, which are paper, cardboard, glass,
plastic, and metal as shown in Figure 4 and Figure 5. Data
augmentation is a method that uses existing training data to create
new training data by applying several changes on the image. As
CNN cannot verify the similarities of images with different
conditions like rotated image, shifted image, flipped image and
so on, data augmentation is useful in improving the accuracy of
CNN model. A neural network library named Keras provides API
(Application Programming Interface) to use data augmentation
when training a model. There will be five main data augmentation
techniques to be used which are image shifts, image flips, image
brightness, image zoom and image rotations.
Figure 4. LabelImg categorization for single object
Figure 5. LabelImg categorization for multiple object
The training process of the object detection model required a
decent GPU in order to have a better mean average precision
result (mAP) and faster loss convergence. The higher computing
power of GPU can increase the speed of training, and large
memory of GPU can include more images to be trained at a time.
Google Colab is chosen to train the CNN object detection model
over a laptop because the GPUs available on Google Colab are
the workstation cards, which are better than notebook GPUs in
many aspects such as performance and memory size and
bandwidth. The interface of Google Colab is shown in Figure 6.
To improve the performance of waste detection model,
hyperparameter tuning can be done with an optimizer. Adam
optimizer will be used to tune the hyperparameters throughout the
training process. Besides, the cosine decay learning rate, in which
the learning rate will decay with the cosine function, is
implemented in the training process to optimize the converging
of the loss. Due to the limitation of the GPU memory, the
optimum batch size of 16 is used. The hyperparameters that can
be tuned with suitable settings are shown in Table IV, and the
training configuration file is shown in Figure 7. In TensorFlow,
the trained model can be exported as an inference graph which
can be used to run object detection with python script. However,
the inference graph cannot be implemented directly in the
TensorFlow Lite interpreter due to the different format of the
model. It must be converted by using TensorFlow Lite
Optimizing Converter (TOCO). The usage of TOCO is required
to build TensorFlow from the computer source.
Figure 6. Interface of Google Colab
Table IV. Settings of Hyperparameter
Hyperparameter
Suggested setting
Learning Rate
Decaying learning rate which has
optimum learning process and
converge
Batch size
Try on 32, 64, 128, 256 and so
forth
Optimizer
Adam optimizer, Momentum
optimizer etc
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Figure 7. Training configuration of Hyperparameter
C. Waste classification and categorization system
The development of waste classification and categorization
required the integration of hardware with the CNN object
detection trained model. The electronic components to be
integrated into this system is listed in Table V, and the diagram
of the electronic component connection is shown in Figure 8. The
type of waste will be categorized for respective compartments,
shown in Table VI. Raspberry Pi 4 acts as the main processing
centre for the waste classification and categorization system. The
trained CNN waste detection model will be imported into
Raspberry Pi 4 and integrated with the algorithms written in
Python language to detect and control the movement of the waste.
Pi Camera is used to work with the trained model to detect the
waste that appeared in the range of the camera module with 8
megapixels. Pi Camera V2 is connected to Raspberry Pi 4 CSI
camera port through the 15-pin connector, which required 3.3V
to work. Apart from that, an ultrasonic sensor, HC-SR04, is used
to detect the non-detectable waste within the waste placement
area; therefore, the waste will be moved into Waste Compartment
4.
Table V. List of electronic components of object detection
Components used
Total
Raspberry Pi 4
1
Pi Camera V2
1
Servo Driver HAT
1
SG-90 Servo Motor
5
HC-SR04 Ultrasonic Sensor
1
11.1V Li-Po Battery
1
Table VI. Type of waste and its compartment
Waste Compartment
Type of Waste
1
Glass, plastic
2
Metal
3
Paper, cardboard
4
Non-detectable waste
Figure 8. Connection diagram of electronic components of object detection
procedure
On the categorization part of the system, servo driver HAT and
servo motors are used to move the waste into the waste
compartment. The gear horn of the SG-90 servo motor is
connected to a plastic board, act as a door to allow the waste to
fall into the respective waste compartment. The SG-90 servo
motor has a torque of 2.5kg/cm, which is sufficient to withstand
most of the waste thrown on the plastic board and able to rotate
clockwise and anticlockwise between 0° and 180° to move the
waste in the desired direction. 4 servo motors are used to control
the plastic board and one servo motor acts as the lock for the
highest door of the bin. As each servo motor requires 5V to
operate, Raspberry Pi 4 lacks sufficient 5V pins and pulse width
modulation (PWM) pin. Therefore, an expansion board, servo
driver HAT is a solution to the limitation of Raspberry Pi 4.
Raspberry Pi 4 uses Pin 3 (SDA) and Pin 4 (SCL) to connect to
servo driver HAT through I2C and is able to control five servo
motors with the available 16 PWM outputs channel. Apart from
that, the HAT is powered from an 11.1V Li-Po battery through
the VIN terminal, which is also able to power on Raspberry Pi 4
through HAT.
D. Bin Status Monitoring and Locker System
The smart waste management system is not limited on classifying
and categorizing the waste; meanwhile, it is able to monitor and
track the condition of the bin from a long distance. Apart from
that, electronics components stored in the top compartment of the
bin are protected by installing RFID based locker system. There
are two parts of the bin status monitoring process, where the bin
acts as the client and the server is connected to the computer. The
system on the bin monitors the status and location of the bin
through sensors, send the information through LoRa
communication and protect the electronic components
compartment with RFID based locker. The server connected to
the computer receives the information from the bin, allowing the
administrator to monitor the bin. The list and connection of
electronic components used are shown in Table VII and Figure
9, respectively.
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Table VII List of components for bin status monitoring
Components used
Total
Arduino Uno R3
1
Dragino LoRa/GPS Shield
1
Dragino LoRa Bee (915 MHz)
1
HC-SR04 Ultrasonic Sensor
4
RC522 RFID Reader
1
12V Solenoid Lock
1
5V Relay Breakout Board
1
11.1V Li-Po Battery
1
12V DC Li-Ion Battery
1
Figure 9. Electronic component connection for bin status monitoring
Arduino Uno R3 acts as the central processing microcontroller
for the ultrasonic sensors, GPS module, LoRa module, RFID
reader and solenoid lock. Arduino Uno has 14 digital input/output
(I/O) pins and six analogue pins, which can perform like digital
pins with certain commands in the Arduino IDE script. With the
pins available on Arduino Uno, it can read from the sensors and
modules to perform the bin monitoring and locker functions. A
12V DC Li-Ion battery is used to supply power to Arduino Uno
through the DC power jack, which will be then regulated down to
5 volts and it is sufficient to supply current to the ultrasonic
sensor, LoRa/GPS shield, RFID reader and a 5V relay breakout
board. The fill level of waste in the bin needed to be monitored in
real-time to improve the waste collection schedule, which
prevents waste overflows or early collection. For this purpose,
HC-SR04 ultrasonic sensors, Dragino Lora/GPS Shield and
Dragino LoRa Bee (915 MHz) are connected to Arduino Uno.
Four ultrasonic sensors are installed in each waste compartment
respectively to monitor the waste fill level in real-time. The
ultrasonic sensor is able to read a distance from 2cm to 400cm
with an accuracy of 0.3cm, which is sufficient to read the fill level
of waste in each waste compartment. The ultrasonic sensor uses
sonar to determine the object distance. Firstly, the trigger pin of
the ultrasonic sensor is set to high to emits a40 kHz high-
frequency sound. The emitted sound wave travels through the air
and bounces back when it meets an object. After 10
microseconds, the trigger pin is set to low and set echo pin to high
using ‘PulseIn’ function of Arduino to measure the duration of
reflected sound waves. The distance of the object can be
calculated by using the measured duration and speed of sound in
the air, which is 343m/s or 0.0343cm/µs at 20℃. The formula is
shown in Equation 1. An expansion board integrated with the
LoRa module and GPS module is installed on Arduino Uno to
track the GPS and transmit data through LoRa communication.
L80 GPS, which is based on MTK MT3339, is used to calculate
and predict the latitude and longitude of the bin by tracking at
least three satellites for positioning. The GPS module can fix the
location in a short amount of time even inside with low battery
consumption due to automatically computed orbits that are saved
for up to 3 days in internal flash. The serial interface UART is set
to 9600 baud rate in the coding and initialized through Software
Serial. The LoRa Bee connected on the shield is based on an
SX1276 transceiver, which can transmit and receive at 915 MHz
with high interference immunity whilst minimizing current
consumption.
𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝒕𝒐 𝒕𝒉𝒆 𝒐𝒃𝒋𝒆𝒄𝒕 = 𝑫𝒖𝒓𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝒓𝒆𝒇𝒍𝒆𝒄𝒕𝒆𝒅 𝒔𝒐𝒖𝒏𝒅×𝟎.𝟎𝟑𝟒𝟑µ𝒔
𝟐
(1)
The electronic components in the smart bin are protected by
integrating an RFID-based locker. Therefore, an RC522 RFID
reader with a registered RFID tag acts as the requirement to
unlock the solenoid locker installed on the door of the electronic
component compartment. The RC522 RFID reader
communicates with the RFID tags with a maximum range of 5cm
by creating a 13.56 MHz electromagnetic field. Arduino Uno
communicates with the reader through Serial Peripheral Interface
(SPI), involving four pins. The 12V solenoid locker is used with
a 5V relay breakout board, which can control the locker to unlock
only when there is an input signal to relay the breakout board.
The input pin (IN) of relay breakout board acts as a switch to
activate the relay with the connection of battery and solenoid
locker to relay common pin (COM) and normally open pin (NO).
The high output signal from Arduino Uno to relay breakout board
enables the current flow into the solenoid locker to unlock it. To
reduce the usage of current, the VCC of the relay breakout board
is connected to an analog pin to disable it when the solenoid
locker without needs to be unlocked. To monitor the status of the
bin from a long distance, a Dragino LoRa shield with LoRa Bee
is used to receive the information transmitted from the bin
through Arduino IDE in the laptop. The LoRa transceiver at both
smart bin and computer operate at 915 MHz to communicate. An
Arduino Uno is required for the installation of a LoRa shield with
a connection to the computer through USB cable type A to enable
the computer to read the information received from the bin. The
computer communicates with Arduino through Serial
communication protocol and Arduino IDE software, where the
baud rate of both computer and Arduino has to be the same. The
baud rate chosen is 9600, which is fixed in the sketch coding in
Arduino and Serial Monitor of Arduino IDE software.
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IV. RESULTS
A. Smart Bin Prototype
The bin prototype is made of acrylic plastic with dimensions
of 0.50 m (length) × 0.50 m (width) × 1.20 m (height). Figure
10(a) and Figure 10(b) are the front view and top view of the
smart bin, respectively labelled with each compartment,
temporary waste placement and electronic components involved.
Figure 10(c) depicts the electronic components used in waste
classification and categorization system. The waste will be
thrown on temporary waste placement for further action. The
camera module from Raspberry Pi will detect the type of waste
using the trained CNN model. The servo motor will control the
plastic board to categorize the waste into the respective
compartment. Table VIII represents the overall function of the
electronic components that have been used in developing the
system.
(a)
(b)
(c)
Figure 10. Perspective view of the smart bin. (a) Top view (b) Front view (c)
List of components
Table VIII. Function of electronic components used
Electronic
component
Function
Raspberry Pi 4
A small single-board computer for CNN based object
detection work with pi camera and ultrasonic sensor to
control servo motors.
Pi Camera V2
Real-time image capturing for process of waste
classification.
Servo Motor on
temporary waste
compartment
Motor acts as locker for the temporary waste
placement.
Servo Motor at waste
compartment
Motor used to control the plastic board acts as door for
each waste compartment.
Servo Driver HAT
Expansion board to control multiple servo motors with
sufficient power supply input with 11.1V Li-Po
battery.
Ultrasonic sensor on
temporary waste
compartment
Detect the common waste not detected by object
detection.
Arduino Uno
A microcontroller board to monitor and send the fill
percentage of waste and GPS location through LoRa
and perform RFID based locker.
LoRa/GPS shield
Able to detect the real-time GPS location and act as
LoRa transmitter.
Ultrasonic sensor at
waste compartment
Monitor the waste fill percentage in waste
compartment.
RFID reader
Read the RFID tag nearby and act as the key to unlock
the locker.
Relay breakout board
A relay to unlock solenoid locker when a HIGH signal
comes from Arduino Uno.
Solenoid locker
Lock the electronic component compartment to
protect the components.
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B. CNN Based Waste Classification and Categorization
The training of the object detection model, SSD MobileNetV2
Quantized 300×300, is performed on the TensorFlow framework
by using Google Colab. The optimal configuration of the training
is shown in Figure 11(a). The model is trained until the
classification loss of the graph is converged and the step of
training is almost 22000, which is shown in Figure 11(b). The
GPU used on Google Colab to train the model is Nvidia Tesla T4.
The training process took 4 hours to converge the loss from 30 to
3.5 exponentially. The loss of the model cannot go lower than
three due to the characteristic of the SSD MobileNet model. The
model has a trade-off inaccuracy for better performance on low-
power computing devices such as Raspberry Pi 4.
(a)
(b)
Figure 11. (a) Optimal configuration (b) Data convergence
There are several losses and mean average precision (mAP) that
have been tracked and visualized through the available toolkit
from TensorBoard. There are two losses that can be tracked in
TensorBoard during the training process, which is classification
loss and total loss. The definition of classification loss is that the
correct category score should be larger by specified safety
margins than all erroneous categories total scores. The graph of
classification loss and the total loss graph are shown in Figure
12(a-b). The x-axis is referring to a number of steps of training,
and the y-axis refers to a different type of loss. As the training
steps increases over time, the loss converges to a small range of
value, which is around 3.5 in this model. When the loss cannot go
lower, the training is stopped and then exported to be used for
detection later. This is to prevent the model the has been
overfitted with the training data, causing unable to detect well on
an object other than training images. The average of AP (average
precision) at all classes with different IoU (Intersection over
union) is the mean average precision (mAP) in Tensorboard. The
graph of mAP shown in Figure 12(c) does not have high accuracy
of up to 80%. This is due to the SSD object detection model's
characteristic, which has lower accuracy than the R-CNN object
detection model. Another reason that leads to the low mAP is the
small size of the dataset, where each class of the dataset consists
of 600 images, including augmented images, as shown in Figure
13.
(a) (b)
(c)
Figure 12. (a) Classification loss, (b) total loss and (c) mean average precision
Figure 13. Samples of augmented images
During the process of training, the model will be saved every 10
minutes. Figure 14(a) shows the training stopped at 21579 steps
where the loss and mAP of the model converge to a fixed value.
The model data files with value 21579 are converted and exported
through TOCO into FlatBuffer format with the extension ‘.tflite’
to be used in object detection later, as shown in Figure 14(b). A
standard text format labelmap file is created, which consists of
the classes name of the waste, as shown in Figure 15. The
labelmap file and exported model file are required by Raspberry
Pi to run detection on the TensorFlow Lite framework. The
screenshot of each class detection is shown in Figure 16. The
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detection's accuracy and speed in terms of FPS (frames per
second) can be seen in the detector windows.
(a)
(b)
Figure 14. (a) Trained model at 21579 steps (b) Trained model converted to
FlatBuffer format
Figure 15. Labelmap for object detection
The average accuracy for each class is calculated in Table IX,
where each test is done with the different waste objects to obtain
the average accuracy. Inference time is the time taken for the
trained model to detect the type of waste that occurred in the
camera. The smaller the value of inference time, the faster the
Raspberry Pi is able to detect the type of waste in real-world
applications. The average inference time of the trained model
integrated with Raspberry Pi is shown in Table X. From the
average result of precision, it is notable that the trained model can
detect the most common waste with average accuracy higher than
80%. The speed of detection of SSD MobileNetV2 Quantized
model in TensorFlow Lite framework performs faster with
similar accuracy compared to SSD MobileNetV2 in TensorFlow
framework on Raspberry Pi. This can improve the user
experience on throwing waste into the bin and the speed to detect
and categorize the waste.
Figure 16. Screenshot of each class detection
Table IX. Average accuracy of each class
Waste
Test
1
Test
2
Test
3
Test
4
Test
5
Average
Precision
Cardboard
88
82
95
88
92
89.0%
Paper
92
86
94
84
92
89.6%
Metal
94
96
95
96
96
95.4%
Plastic
96
95
90
82
88
90.2%
Glass
96
94
95
94
94
94.6%
Table X. Average inference time
Waste
Test 1
(ms)
Test 2
(ms)
Test 3
(ms)
Average
Inference
Time (ms)
Cardboard
358.021
359.201
358.500
358.574
Paper
358.787
359.696
359.078
359.187
Metal
360.234
359.262
359.621
359.706
Plastic
358.616
357.632
359.687
358.645
Glass
358.260
357.457
360.343
358.687
C. Bin Status Monitoring and Locker System
The bin status monitoring part of the system consists of obtaining
the latitude and longitude of the bin, waste fill percentage and
send this information to the receiver through the LoRa module.
The GPS module on Dragino LoRa/GPS shield requires tracking
three satellites for positioning. In real-world application, the GPS
module takes 5 to 20 minutes to fix the position indoor and 1
minute for outdoor. However, the time needed to fix the position
will be improved later with the help of internal memory flash,
which can store up to 3 days of computed orbits, which are
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latitude and longitude. The green LED on the shield will start
blinking when the GPS location is fixed, as shown in Figure
17(a). The integration of hardware, LoRa/GPS shield and
ultrasonic sensors and algorithm written in Arduino IDE sketch
can send the GPS location and waste fill percentage through
LoRa transceiver. However, there are several conditions
implemented to save energy and ensure the message is sent to the
receiver successfully. Firstly, the data will not be sent when the
GPS position is not yet fixed, as shown in Figure 17(b). This is
to reduce the energy consumption of the LoRa module. Next, the
algorithm in smart bin’s Arduino waits for the reply from the
receiver to ensure the message is received successfully for 10
seconds, as shown in Figure 17(c).
(a) (b)
(c)
Figure 17. (a) GPS-Sheild connection (b) GPS status (c) LoRa receiving
message
By using Arduino IDE software, the information received by the
LoRa receiver connected to the computer can be monitored
through Serial Monitor in the software at a baud rate of 9600.
Figure 18 shows the screenshot for Arduino on smart bin, where
the GPS location and waste fill percentage are captured
successfully by the sensors. The information is successfully sent
with a reply from the LoRa receiver. On the LoRa receiver
connected to the computer, Figures 19(a-b) shows that the
message is successfully received and displayed in Serial Monitor.
The latitude and longitude received on the computer are inserted
into Google Maps, and the location tracking is precise, as shown
in Figure 19(c). The integration of the RFID reader and solenoid
locker on Arduino is installed on the smart bin, as shown in
Figure 20(a). The RFID will be functioning for 30 seconds and
then switched off for the process to monitor and send the
information of the bin. This process will be loop continuously
until the RFID reader reads a registered RFID tag, then it will
break the loop and unlock the solenoid locker for 5 seconds. The
unlocking process is displayed in Serial Monitor, as shown in
Figure 20(b).
Figure 18. Sending information
(a) (b)
(c)
Figure 19. (a) LoRa receiver message (b) Blinking status (c) Latitude and
Longitude on Google Maps
(a)
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(b)
Figure 20. (a) Solenoid locker (b) Unlocking process of RFID based locker
V. DISCUSSION
Table XI presents the comparison among different waste
management systems in terms of the type of waste, sensors,
communication protocol, micro-controller, and machine learning
architecture. Based on the comparison, it is observed that the
existing system 1 to 6 is only capable of monitoring the condition
of the bin, such as waste fill percentage, gas pollution level and
GPS location. Most of the communication protocols used are not
suitable for implementation in smart waste management systems
due to the short distance of transmission, and protocols such as
GSM are terminating soon in many countries. The existing
system 7 to 10 involves machine learning to classify the type of
waste. Only one system has a mechanism to categorize the waste;
however, it can only detect plastic waste. Based on the
comparison, the existing waste management system cannot cover
most of the significant challenges faced in urban areas.
VI. CONCLUSION
The first problem faced by the current waste management system
in cities is inappropriate use of recycle bins, causing high waste
generation. This problem can be reduced through the automation
process of waste classification and categorization in the bin. With
the integration of the CNN model, SSD MobileNetV2 Quantized
300x300 and Pi Camera on Raspberry Pi, the bin is able to
classify five types of waste, including paper, cardboard, plastic,
glass and metal, with acceptable precision and low interference
time. The servo motors connected to the plastic board in the smart
bin categorize the waste from temporary waste placement into the
respective waste compartment. The process to classify and
categorize the waste took 4 seconds and can be improved in
future. The second challenge faced is the waste of resources such
as manpower due to the fixed schedule-based waste collection.
This challenge can be reduced by implementing a system able to
monitor the status of the bin from a far distance. The bin status
monitoring system in the smart bin is able to improve the waste
of resources due to schedule based waste collection. The
ultrasonic sensors connected to Arduino Uno can detect the waste
fill percentage with precise reading. The GPS module on
LoRa/GPS shield can detect the latitude and longitude accurately
and quickly. With the LoRa module, both waste fill percentage
and GPS location can be sent to the LoRa receiver connected to
the laptop with a distance of up to 5 km. This can help the waste
management system administrator monitor the status of the bin
from a far distance and decide the time to collect waste from the
bin. In order to protect the electronic components in the smart bin,
an RFID reader and solenoid locker are installed on the top
compartment and connected to Arduino Uno. Only the registered
RFID tag can unlock the solenoid locker to allow further
maintenance and upgrade of the system in future.
Several limitations exist in the proposed system. Firstly, the
small dataset can hardly improve the CNN-based object detection
model to detect more waste precisely but only five types of
common waste. Apart from that, the object detection model with
higher precision is not able to be implemented on Raspberry Pi
without GPU. Lastly, the usage of batteries in the system requires
the renewal of batteries after a period. The proposed system can
be improved by increasing the size of the dataset by adding more
variants of waste images in each class and increase the types of
waste to expand the coverage of waste detectable. Apart from
that, implementing an object detection model with higher
precision and speed on a microcontroller with higher processing
power can improve waste detection and categorization
performance. The system's power source can be changed to
renewable energy sources such as solar panels to improve the
lifetime of the system.
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Table XI. Comparison among different waste management systems with the type of waste, sensors, communication protocol, micro-controller and machine
learning algorithm
No
Ref.
Object
Detection
Model
Type of waste
detectable
Micro-
controller used
Waste
categorizatio
n
mechanism
Communication
protocol
Sensor used
Locker
system
1
[17]
-
Common waste
NodeMCU
Controller
-
GSM
Ultrasonic sensor, GPS
module
-
2
[40]
-
-Common waste
PIC18
microcontroller
-
-
Ultrasonic sensor, PIR
sensor, Gas sensor,
Load Cell sensor
-
3
[16]
-
Common waste
Arduino Uno,
Node MCU
-
GSM
Ultrasonic sensor,
DHT11 sensor
-
4
[21]
-
Common waste
Arduino Pro
Mini
-
Wi-Fi
Ultrasonic sensor,
stinky gas sensor, MQ-
135, MQ-136
-
5
[26]
-
Common waste
Arduino Uno
-
GSM
Ultrasonic sensor
-
6
[27]
-
Common waste
Arduino Uno
-
Wi-Fi
Ultrasonic sensor,
RFID reader
-
7
[33]
Modified
AlexNet
Plastic
-
Yes
-
-
-
8
[34]
Faster R-
CNN
Refrigerators,
washing machines
and monitors
-
-
-
-
-
9
[35]
WasteNet
Paper, glass, metal,
plastic, cardboard
and trash
-
-
-
-
-
10
[36]
SVM and
11-layer
CNN
Paper, glass, metal,
plastic, cardboard
and trash
-
-
-
-
-
11
Proposed
system
SSD
MobileNetV
2 Quantized
Paper, cardboard,
glass, plastic, metal
Raspberry Pi
4, Arduino Uno
R3
Yes
LoRa
Ultrasonic sensor,
GPS module, RFID
reader
Yes
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